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| Regressziós diszkontinuitási dizájn (RDD)× | Tárgyhajlamossági pontszám illesztés× | |
|---|---|---|
| Tudományterület≠ | Oksági következtetés | Kutatási statisztika |
| Módszercsalád≠ | Regression model | Process / pipeline |
| Keletkezés éve≠ | 2008 | 1983 |
| Megalkotó≠ | Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction) | Paul Rosenbaum and Donald Rubin |
| Típus≠ | Quasi-experimental causal design | Method |
| Alapmű≠ | Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗ | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. DOI ↗ |
| Alternatív nevek≠ | RDD, regression discontinuity design, sharp RDD, fuzzy RDD | PSM, propensity score weighting, covariate balance |
| Kapcsolódó≠ | 5 | 3 |
| Összefoglaló≠ | Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold. | Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias. |
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